| 2470 |
10.1007/s00332-018-9525-3 |
Machine Learning Approximation Algorithms For
High-Dimensional Fully Nonlinear Partial Differential Equations And
Second-Order Backward Stochastic Differential Equations |
Beck, Christian, 0000-0002-3609-7778; E, Weinan;
Jentzen, Arnulf |
Journal Of Nonlinear Science |
2019 |
| 2527 |
10.1007/s00466-019-01740-0 |
Prediction Of Aerodynamic Flow Fields Using
Convolutional Neural Networks |
Bhatnagar, Saakaar; Afshar, Yaser; Pan, Shaowu;
Duraisamy, Karthik; Kaushik, Shailendra |
Computational Mechanics |
2019 |
| 4900 |
10.1016/j.cma.2018.07.017 |
Reduced Order Modeling For Nonlinear Structural
Analysis Using Gaussian Process Regression |
Guo, Mengwu, 0000-0002-5541-437X; Hesthaven, Jan
S. |
Computer Methods In Applied Mechanics And
Engineering |
2018 |
| 4906 |
10.1016/j.cma.2018.10.029 |
Data-Driven Reduced Order Modeling For Time-Dependent
Problems |
Guo, Mengwu, 0000-0002-5541-437X; Hesthaven, Jan
S. |
Computer Methods In Applied Mechanics And
Engineering |
2019 |
| 4908 |
10.1016/j.cma.2019.112623 |
Machine Learning In Cardiovascular Flows Modeling:
Predicting Arterial Blood Pressure From Non-Invasive 4D Flow Mri Data
Using Physics-Informed Neural Networks |
Kissas, Georgios; Yang, Yibo; Hwuang, Eileen; Witschey,
Walter R.; Detre, John A.; Perdikaris, Paris, 0000-0002-2816-3229 |
Computer Methods In Applied Mechanics And
Engineering |
2020 |
| 4909 |
10.1016/j.cma.2019.112732 |
Surrogate Modeling For Fluid Flows Based On
Physics-Constrained Deep Learning Without Simulation Data |
Sun, Luning; Gao, Han; Pan, Shaowu,
0000-0002-2462-362X; Wang, Jian-Xun, 0000-0002-9030-1733 |
Computer Methods In Applied Mechanics And
Engineering |
2020 |
| 4910 |
10.1016/j.cma.2019.112789 |
Physics-Informed Neural Networks For High-Speed
Flows |
Mao, Zhiping; Jagtap, Ameya D.; Karniadakis, George
Em |
Computer Methods In Applied Mechanics And
Engineering |
2020 |
| 5037 |
10.1016/j.compfluid.2018.07.021 |
Projection-Based Model Reduction: Formulations For
Physics-Based Machine Learning |
Swischuk, Renee; Mainini, Laura, 0000-0002-5969-9069;
Peherstorfer, Benjamin; Willcox, Karen |
Computers & Fluids |
2019 |
| 7252 |
10.1016/j.jcp.2018.04.018 |
Bayesian Deep Convolutional
Encoder<U+2013>Decoder Networks For Surrogate Modeling And
Uncertainty Quantification |
Zhu, Yinhao; Zabaras, Nicholas |
Journal Of Computational Physics |
2018 |
| 7253 |
10.1016/j.jcp.2018.08.029 |
Dgm: A Deep Learning Algorithm For Solving Partial
Differential Equations |
Sirignano, Justin; Spiliopoulos, Konstantinos |
Journal Of Computational Physics |
2018 |
| 7254 |
10.1016/j.jcp.2018.08.036 |
Deep Uq: Learning Deep Neural Network Surrogate Models
For High Dimensional Uncertainty Quantification |
Tripathy, Rohit K.; Bilionis, Ilias |
Journal Of Computational Physics |
2018 |
| 7255 |
10.1016/j.jcp.2018.10.045 |
Physics-Informed Neural Networks: A Deep Learning
Framework For Solving Forward And Inverse Problems Involving Nonlinear
Partial Differential Equations |
Raissi, M., 0000-0002-8467-4568; Perdikaris, P.,
0000-0002-2816-3229; Karniadakis, G.E. |
Journal Of Computational Physics |
2019 |
| 7256 |
10.1016/j.jcp.2019.01.031 |
Non-Intrusive Reduced Order Modeling Of Unsteady Flows
Using Artificial Neural Networks With Application To A Combustion
Problem |
Wang, Qian, 0000-0001-5409-1663; Hesthaven, Jan S.,
0000-0001-8074-1586; Ray, Deep, 0000-0002-8460-9862 |
Journal Of Computational Physics |
2019 |
| 7257 |
10.1016/j.jcp.2019.05.024 |
Physics-Constrained Deep Learning For High-Dimensional
Surrogate Modeling And Uncertainty Quantification Without Labeled
Data |
Zhu, Yinhao, 0000-0002-9435-4576; Zabaras, Nicholas,
0000-0003-3144-8388; Koutsourelakis, Phaedon-Stelios,
0000-0002-9345-759X; Perdikaris, Paris, 0000-0002-2816-3229 |
Journal Of Computational Physics |
2019 |
| 7258 |
10.1016/j.jcp.2019.05.027 |
Adversarial Uncertainty Quantification In
Physics-Informed Neural Networks |
Yang, Yibo; Perdikaris, Paris, 0000-0002-2816-3229 |
Journal Of Computational Physics |
2019 |
| 7259 |
10.1016/j.jcp.2019.07.048 |
Quantifying Total Uncertainty In Physics-Informed
Neural Networks For Solving Forward And Inverse Stochastic Problems |
Zhang, Dongkun; Lu, Lu; Guo, Ling; Karniadakis, George
Em |
Journal Of Computational Physics |
2019 |
| 7260 |
10.1016/j.jcp.2019.108910 |
Deep Neural Networks For Data-Driven Les Closure
Models |
Beck, Andrea, 0000-0003-3634-7447; Flad, David; Munz,
Claus-Dieter |
Journal Of Computational Physics |
2019 |
| 7261 |
10.1016/j.jcp.2019.108925 |
Pde-Net 2.0: Learning Pdes From Data With A
Numeric-Symbolic Hybrid Deep Network |
Long, Zichao; Lu, Yiping; Dong, Bin |
Journal Of Computational Physics |
2019 |
| 7262 |
10.1016/j.jcp.2019.109020 |
A Composite Neural Network That Learns From
Multi-Fidelity Data: Application To Function Approximation And Inverse
Pde Problems |
Meng, Xuhui; Karniadakis, George Em |
Journal Of Computational Physics |
2020 |
| 7263 |
10.1016/j.jcp.2019.109136 |
Adaptive Activation Functions Accelerate Convergence In
Deep And Physics-Informed Neural Networks |
Jagtap, Ameya D., 0000-0002-8831-1000; Kawaguchi,
Kenji; Karniadakis, George Em |
Journal Of Computational Physics |
2020 |
| 8610 |
10.1016/j.neucom.2018.06.056 |
A Unified Deep Artificial Neural Network Approach To
Partial Differential Equations In Complex Geometries |
Berg, Jens, 0000-0003-3008-8915; Nystrom, Kaj |
Neurocomputing |
2018 |
| 8757 |
10.1016/j.paerosci.2018.10.001 |
Quantification Of Model Uncertainty In Rans
Simulations: A Review |
Xiao, Heng, 0000-0002-3323-4028; Cinnella, Paola |
Progress In Aerospace Sciences |
2019 |
| 10599 |
10.1017/jfm.2018.770 |
Subgrid Modelling For Two-Dimensional Turbulence Using
Neural Networks |
Maulik, R.; San, O., 0000-0002-2241-4648; Rasheed, A.;
Vedula, P. |
Journal Of Fluid Mechanics |
2018 |
| 10600 |
10.1017/jfm.2018.872 |
Deep Learning Of Vortex-Induced Vibrations |
Raissi, Maziar, 0000-0002-8467-4568; Wang, Zhicheng,
0000-0002-5856-6459; Triantafyllou, Michael S., 0000-0002-4960-7060;
Karniadakis, George Em |
Journal Of Fluid Mechanics |
2018 |
| 10601 |
10.1017/jfm.2019.238 |
Super-Resolution Reconstruction Of Turbulent Flows With
Machine Learning |
Fukami, Kai; Fukagata, Koji, 0000-0003-4805-238X;
Taira, Kunihiko, 0000-0002-3762-8075 |
Journal Of Fluid Mechanics |
2019 |
| 10603 |
10.1017/jfm.2019.62 |
Artificial Neural Networks Trained Through Deep
Reinforcement Learning Discover Control Strategies For Active Flow
Control |
Rabault, Jean, 0000-0002-7244-6592; Kuchta, Miroslav;
Jensen, Atle; Reglade, Ulysse; Cerardi, Nicolas |
Journal Of Fluid Mechanics |
2019 |
| 10604 |
10.1017/jfm.2019.700 |
Data-Driven Prediction Of Unsteady Flow Over A Circular
Cylinder Using Deep Learning |
Lee, Sangseung, 0000-0001-7341-8289; You, Donghyun,
0000-0003-2470-5411 |
Journal Of Fluid Mechanics |
2019 |
| 12482 |
10.1029/2018wr023528 |
|
Mo, Shaoxing, 0000-0003-2831-4805; Zhu, Yinhao;
Zabaras, Nicholas, 0000-0003-3144-8388; Shi, Xiaoqing,
0000-0002-5074-8856; Wu, Jichun, 0000-0001-9799-6745 |
Water Resources Research |
2019 |
| 12483 |
10.1029/2018wr024638 |
|
Mo, Shaoxing, 0000-0003-2831-4805; Zabaras, Nicholas,
0000-0003-3144-8388; Shi, Xiaoqing, 0000-0002-5074-8856; Wu, Jichun,
0000-0001-9799-6745 |
Water Resources Research |
2019 |
| 16291 |
10.1063/1.5061693 |
Machine Learning Methods For Turbulence Modeling In
Subsonic Flows Around Airfoils |
Zhu, Linyang; Zhang, Weiwei; Kou, Jiaqing,
0000-0002-0965-5404; Liu, Yilang |
Physics Of Fluids |
2019 |
| 16312 |
10.1063/1.5094943 |
Fast Flow Field Prediction Over Airfoils Using Deep
Learning Approach |
Sekar, Vinothkumar, 0000-0001-5734-550X; Khoo, Boo
Cheong, 0000-0003-4710-4598 |
Physics Of Fluids |
2019 |
| 16322 |
10.1063/1.5113494 |
A Deep Learning Enabler For Nonintrusive Reduced Order
Modeling Of Fluid Flows |
Pawar, S., 0000-0001-7562-799X; Rahman, S. M.,
0000-0003-0996-6883; Vaddireddy, H.; San, O., 0000-0002-2241-4648;
Rasheed, A.; Vedula, P. |
Physics Of Fluids |
2019 |
| 16359 |
10.1073/pnas.1718942115 |
Solving High-Dimensional Partial Differential Equations
Using Deep Learning |
Han, Jiequn, 0000-0002-3553-7313; Jentzen, Arnulf; E,
Weinan |
Proceedings Of The National Academy Of Sciences |
2018 |
| 17759 |
10.1103/physrevfluids.3.074602 |
Physics-Informed Machine Learning Approach For
Augmenting Turbulence Models: A Comprehensive Framework |
Wu, Jin-Long; Xiao, Heng; Paterson, Eric |
Physical Review Fluids |
2018 |
| 17760 |
10.1103/physrevfluids.4.034602 |
Predictive Large-Eddy-Simulation Wall Modeling Via
Physics-Informed Neural Networks |
Yang, X. I. A.; Zafar, S.; Wang, J.-X.; Xiao, H. |
Physical Review Fluids |
2019 |
| 17761 |
10.1103/physrevfluids.4.054603 |
Predictions Of Turbulent Shear Flows Using Deep Neural
Networks |
Srinivasan, P. A.; Guastoni, L.; Azizpour, H.;
Schlatter, P.; Vinuesa, R. |
Physical Review Fluids |
2019 |
| 17762 |
10.1103/physrevfluids.4.100501 |
Perspective On Machine Learning For Advancing Fluid
Mechanics |
Brenner, M. P.; Eldredge, J. D.; Freund, J. B. |
Physical Review Fluids |
2019 |
| 18889 |
10.1126/science.aaw4741 |
Hidden Fluid Mechanics: Learning Velocity And Pressure
Fields From Flow Visualizations |
Raissi, Maziar, 0000-0002-8467-4568; Yazdani, Alireza,
0000-0002-0139-2080; Karniadakis, George Em, 0000-0002-9713-7120 |
Science |
2020 |
| 19292 |
10.1137/18m1191944 |
Data-Driven Identification Of Parametric Partial
Differential Equations |
Rudy, Samuel; Alla, Alessandro; Brunton, Steven L.;
Kutz, J. Nathan |
Siam Journal On Applied Dynamical Systems |
2019 |
| 19293 |
10.1137/18m1229845 |
Fpinns: Fractional Physics-Informed Neural
Networks |
Pang, Guofei; Lu, Lu, 0000-0002-5476-5768; Karniadakis,
George Em, 0000-0002-9713-7120 |
Siam Journal On Scientific Computing |
2019 |
| 19294 |
10.1137/19m1274067 |
Deepxde: A Deep Learning Library For Solving
Differential Equations |
Lu, Lu, 0000-0002-5476-5768; Meng, Xuhui; Mao, Zhiping;
Karniadakis, George Em, 0000-0002-9713-7120 |
Siam Review |
2021 |
| 19378 |
10.1146/annurev-fluid-010518-040547 |
Turbulence Modeling In The Age Of Data |
Duraisamy, Karthik; Iaccarino, Gianluca; Xiao,
Heng |
Annual Review Of Fluid Mechanics |
2019 |
| 19379 |
10.1146/annurev-fluid-010719-060214 |
Machine Learning For Fluid Mechanics |
Brunton, Steven L.; Noack, Bernd R.; Koumoutsakos,
Petros |
Annual Review Of Fluid Mechanics |
2020 |
| 20858 |
10.2514/1.j058462 |
Modal Analysis Of Fluid Flows: Applications And
Outlook |
Taira, Kunihiko; Hemati, Maziar S.; Brunton, Steven L.;
Sun, Yiyang; Duraisamy, Karthik; Bagheri, Shervin; Dawson, Scott T. M.;
Yeh, Chi-An |
Aiaa Journal |
2020 |